{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HW1: 线性回归"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1、导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  # 矩阵操作\n",
    "import pandas as pd # SQL数据处理\n",
    "\n",
    "from sklearn.metrics import r2_score  #评价回归预测模型的性能\n",
    "\n",
    "import matplotlib.pyplot as plt   #画图\n",
    "import seaborn as sns\n",
    "\n",
    "# 图形出现在Notebook里而不是新窗口\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2、数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读入数据\n",
    "dpath = './Bike-Sharing-Dataset/'\n",
    "data = pd.read_csv(dpath + \"day.csv\")\n",
    "\n",
    "# 观察前5行，了解数据每列（特征）的概况\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "小结：\n",
    "1、部分特征为类别特征，需要进行数值化，如，季节Season、月份mnth、星期中的哪天weekday、天气weathersit；\n",
    "2、日期字符串特征包含年份、月份、当月第几天信息，当前已有年份和月份特征，并且它只是代表了样本的索引，没什么实质含义，可以删除日期属性;\n",
    "3、序号instance无实际含义，可以删除此特征。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 训练数据和测试数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3694: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    }
   ],
   "source": [
    "# 训练数据为2011年数据，测试数据为2012年数据\n",
    "train_data = data.loc[data.yr == 0]\n",
    "test_data = data.loc[data.yr == 1]\n",
    "\n",
    "train_data.drop(['casual', 'registered'], axis = 1, inplace=True)\n",
    "test_data.drop(['casual', 'registered'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  2.2 数据基本信息\n",
    "样本数目、特征维数\n",
    "每个特征的类型、空值样本的数目、数据类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(366, 14)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 365 entries, 0 to 364\n",
      "Data columns (total 14 columns):\n",
      "instant       365 non-null int64\n",
      "dteday        365 non-null object\n",
      "season        365 non-null int64\n",
      "yr            365 non-null int64\n",
      "mnth          365 non-null int64\n",
      "holiday       365 non-null int64\n",
      "weekday       365 non-null int64\n",
      "workingday    365 non-null int64\n",
      "weathersit    365 non-null int64\n",
      "temp          365 non-null float64\n",
      "atemp         365 non-null float64\n",
      "hum           365 non-null float64\n",
      "windspeed     365 non-null float64\n",
      "cnt           365 non-null int64\n",
      "dtypes: float64(4), int64(9), object(1)\n",
      "memory usage: 42.8+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 366 entries, 365 to 730\n",
      "Data columns (total 14 columns):\n",
      "instant       366 non-null int64\n",
      "dteday        366 non-null object\n",
      "season        366 non-null int64\n",
      "yr            366 non-null int64\n",
      "mnth          366 non-null int64\n",
      "holiday       366 non-null int64\n",
      "weekday       366 non-null int64\n",
      "workingday    366 non-null int64\n",
      "weathersit    366 non-null int64\n",
      "temp          366 non-null float64\n",
      "atemp         366 non-null float64\n",
      "hum           366 non-null float64\n",
      "windspeed     366 non-null float64\n",
      "cnt           366 non-null int64\n",
      "dtypes: float64(4), int64(9), object(1)\n",
      "memory usage: 42.9+ KB\n"
     ]
    }
   ],
   "source": [
    "test_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "小结：\n",
    "1、数据集共有731组数据,训练集365组，测试集366组，13个数据特征；\n",
    "2、数据无缺失，无需进行填补。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 数据探索\n",
    "查看数据各特征的分布，以及特征之间是否存在相关关系等冗余。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3694: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
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       "      <th>atemp</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>1349</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  mnth  holiday  weekday  workingday  weathersit      temp     atemp  \\\n",
       "0       1     1        0        6           0           2  0.344167  0.363625   \n",
       "1       1     1        0        0           0           2  0.363478  0.353739   \n",
       "2       1     1        0        1           1           1  0.196364  0.189405   \n",
       "3       1     1        0        2           1           1  0.200000  0.212122   \n",
       "4       1     1        0        3           1           1  0.226957  0.229270   \n",
       "\n",
       "        hum  windspeed   cnt  \n",
       "0  0.805833   0.160446   985  \n",
       "1  0.696087   0.248539   801  \n",
       "2  0.437273   0.248309  1349  \n",
       "3  0.590435   0.160296  1562  \n",
       "4  0.436957   0.186900  1600  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 并去掉日期、序号和年份特征列\n",
    "# 训练集\n",
    "train_data.drop(['dteday', 'instant', 'yr'], axis = 1, inplace=True)\n",
    "train_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3694: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    },
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       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
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       "      <th>atemp</th>\n",
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       "      <th>365</th>\n",
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       "      <td>1</td>\n",
       "      <td>0.273043</td>\n",
       "      <td>0.252304</td>\n",
       "      <td>0.381304</td>\n",
       "      <td>0.329665</td>\n",
       "      <td>1951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.126275</td>\n",
       "      <td>0.441250</td>\n",
       "      <td>0.365671</td>\n",
       "      <td>2236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.107500</td>\n",
       "      <td>0.119337</td>\n",
       "      <td>0.414583</td>\n",
       "      <td>0.184700</td>\n",
       "      <td>2368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.265833</td>\n",
       "      <td>0.278412</td>\n",
       "      <td>0.524167</td>\n",
       "      <td>0.129987</td>\n",
       "      <td>3272</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     season  mnth  holiday  weekday  workingday  weathersit      temp  \\\n",
       "365       1     1        0        0           0           1  0.370000   \n",
       "366       1     1        1        1           0           1  0.273043   \n",
       "367       1     1        0        2           1           1  0.150000   \n",
       "368       1     1        0        3           1           2  0.107500   \n",
       "369       1     1        0        4           1           1  0.265833   \n",
       "\n",
       "        atemp       hum  windspeed   cnt  \n",
       "365  0.375621  0.692500   0.192167  2294  \n",
       "366  0.252304  0.381304   0.329665  1951  \n",
       "367  0.126275  0.441250   0.365671  2236  \n",
       "368  0.119337  0.414583   0.184700  2368  \n",
       "369  0.278412  0.524167   0.129987  3272  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 去掉日期特征\n",
    "# 测试集\n",
    "test_data.drop(['dteday', 'instant', 'yr'], axis = 1, inplace=True)\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if sys.path[0] == '':\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  del sys.path[0]\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:25: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:26: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:28: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:32: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3694: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "      <th>spring</th>\n",
       "      <th>summer</th>\n",
       "      <th>autumn</th>\n",
       "      <th>...</th>\n",
       "      <th>sunny</th>\n",
       "      <th>fog</th>\n",
       "      <th>light_snow</th>\n",
       "      <th>Monday</th>\n",
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       "      <th>Wednesday</th>\n",
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       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   holiday  workingday      temp     atemp       hum  windspeed   cnt  spring  \\\n",
       "0        0           0  0.344167  0.363625  0.805833   0.160446   985     1.0   \n",
       "1        0           0  0.363478  0.353739  0.696087   0.248539   801     1.0   \n",
       "2        0           1  0.196364  0.189405  0.437273   0.248309  1349     1.0   \n",
       "3        0           1  0.200000  0.212122  0.590435   0.160296  1562     1.0   \n",
       "4        0           1  0.226957  0.229270  0.436957   0.186900  1600     1.0   \n",
       "\n",
       "   summer  autumn   ...    sunny  fog  light_snow  Monday  Tuesday  Wednesday  \\\n",
       "0     0.0     0.0   ...      0.0  1.0         0.0     0.0      0.0        0.0   \n",
       "1     0.0     0.0   ...      0.0  1.0         0.0     1.0      0.0        0.0   \n",
       "2     0.0     0.0   ...      1.0  0.0         0.0     0.0      1.0        0.0   \n",
       "3     0.0     0.0   ...      1.0  0.0         0.0     0.0      0.0        1.0   \n",
       "4     0.0     0.0   ...      1.0  0.0         0.0     0.0      0.0        0.0   \n",
       "\n",
       "   Thursday  Friday  Saturday  Sunday  \n",
       "0       0.0     0.0       0.0     1.0  \n",
       "1       0.0     0.0       0.0     0.0  \n",
       "2       0.0     0.0       0.0     0.0  \n",
       "3       0.0     0.0       0.0     0.0  \n",
       "4       1.0     0.0       0.0     0.0  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 独热编码数值化类别特征\n",
    "# 测试集\n",
    "from sklearn import preprocessing  \n",
    "enc_train = preprocessing.OneHotEncoder()\n",
    "enc_train.fit(train_data[['season', 'mnth', 'weathersit', 'weekday']].values)\n",
    "Temp_train = enc_train.transform(train_data[['season', 'mnth', 'weathersit', 'weekday']].values).toarray()\n",
    "train_data['spring'] = Temp_train[:,0]\n",
    "train_data['summer'] = Temp_train[:,1]\n",
    "train_data['autumn'] = Temp_train[:,2]\n",
    "train_data['winter'] = Temp_train[:,3]\n",
    "train_data['January'] = Temp_train[:,4]\n",
    "train_data['February'] = Temp_train[:,5]\n",
    "train_data['March'] = Temp_train[:,6]\n",
    "train_data['April'] = Temp_train[:,7]\n",
    "train_data['May'] = Temp_train[:,8]\n",
    "train_data['June'] = Temp_train[:,9]\n",
    "train_data['July'] = Temp_train[:,10]\n",
    "train_data['August'] = Temp_train[:,11]\n",
    "train_data['September'] = Temp_train[:,12]\n",
    "train_data['October'] = Temp_train[:,13]\n",
    "train_data['November'] = Temp_train[:,14]\n",
    "train_data['December'] = Temp_train[:,15]\n",
    "train_data['sunny'] = Temp_train[:,16]\n",
    "train_data['fog'] = Temp_train[:,17]\n",
    "train_data['light_snow'] = Temp_train[:,18]\n",
    "train_data['Monday'] = Temp_train[:,19]\n",
    "train_data['Tuesday'] = Temp_train[:,20]\n",
    "train_data['Wednesday'] = Temp_train[:,21]\n",
    "train_data['Thursday'] = Temp_train[:,22]\n",
    "train_data['Friday'] = Temp_train[:,23]\n",
    "train_data['Saturday'] = Temp_train[:,24]\n",
    "train_data['Sunday'] = Temp_train[:,25]\n",
    "\n",
    "train_data.drop(['season', 'mnth', 'weathersit', 'weekday'], axis = 1, inplace=True)\n",
    "\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if sys.path[0] == '':\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  del sys.path[0]\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:25: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:26: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:28: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3694: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "      <th>spring</th>\n",
       "      <th>summer</th>\n",
       "      <th>autumn</th>\n",
       "      <th>...</th>\n",
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       "      <th>Sunday</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "<p>5 rows × 33 columns</p>\n",
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      ],
      "text/plain": [
       "     holiday  workingday      temp     atemp       hum  windspeed   cnt  \\\n",
       "365        0           0  0.370000  0.375621  0.692500   0.192167  2294   \n",
       "366        1           0  0.273043  0.252304  0.381304   0.329665  1951   \n",
       "367        0           1  0.150000  0.126275  0.441250   0.365671  2236   \n",
       "368        0           1  0.107500  0.119337  0.414583   0.184700  2368   \n",
       "369        0           1  0.265833  0.278412  0.524167   0.129987  3272   \n",
       "\n",
       "     spring  summer  autumn   ...    sunny  fog  light_snow  Monday  Tuesday  \\\n",
       "365     1.0     0.0     0.0   ...      1.0  0.0         0.0     1.0      0.0   \n",
       "366     1.0     0.0     0.0   ...      1.0  0.0         0.0     0.0      1.0   \n",
       "367     1.0     0.0     0.0   ...      1.0  0.0         0.0     0.0      0.0   \n",
       "368     1.0     0.0     0.0   ...      0.0  1.0         0.0     0.0      0.0   \n",
       "369     1.0     0.0     0.0   ...      1.0  0.0         0.0     0.0      0.0   \n",
       "\n",
       "     Wednesday  Thursday  Friday  Saturday  Sunday  \n",
       "365        0.0       0.0     0.0       0.0     0.0  \n",
       "366        0.0       0.0     0.0       0.0     0.0  \n",
       "367        1.0       0.0     0.0       0.0     0.0  \n",
       "368        0.0       1.0     0.0       0.0     0.0  \n",
       "369        0.0       0.0     1.0       0.0     0.0  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 独热编码数值化类别特征\n",
    "# 训练集\n",
    "enc_test = preprocessing.OneHotEncoder()\n",
    "enc_test.fit(test_data[['season', 'mnth', 'weathersit', 'weekday']].values)\n",
    "Temp_test = enc_test.transform(test_data[['season', 'mnth', 'weathersit', 'weekday']].values).toarray()\n",
    "test_data['spring'] = Temp_test[:,0]\n",
    "test_data['summer'] = Temp_test[:,1]\n",
    "test_data['autumn'] = Temp_test[:,2]\n",
    "test_data['winter'] = Temp_test[:,3]\n",
    "test_data['January'] = Temp_test[:,4]\n",
    "test_data['February'] = Temp_test[:,5]\n",
    "test_data['March'] = Temp_test[:,6]\n",
    "test_data['April'] = Temp_test[:,7]\n",
    "test_data['May'] = Temp_test[:,8]\n",
    "test_data['June'] = Temp_test[:,9]\n",
    "test_data['July'] = Temp_test[:,10]\n",
    "test_data['August'] = Temp_test[:,11]\n",
    "test_data['September'] = Temp_test[:,12]\n",
    "test_data['October'] = Temp_test[:,13]\n",
    "test_data['November'] = Temp_test[:,14]\n",
    "test_data['December'] = Temp_test[:,15]\n",
    "test_data['sunny'] = Temp_test[:,16]\n",
    "test_data['fog'] = Temp_test[:,17]\n",
    "test_data['light_snow'] = Temp_test[:,18]\n",
    "test_data['Monday'] = Temp_test[:,19]\n",
    "test_data['Tuesday'] = Temp_test[:,20]\n",
    "test_data['Wednesday'] = Temp_test[:,21]\n",
    "test_data['Thursday'] = Temp_test[:,22]\n",
    "test_data['Friday'] = Temp_test[:,23]\n",
    "test_data['Saturday'] = Temp_test[:,24]\n",
    "test_data['Sunday'] = Temp_test[:,25]\n",
    "\n",
    "test_data.drop(['season', 'mnth', 'weathersit', 'weekday'], axis = 1, inplace=True)\n",
    "\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数值型特征归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从原始数据中分离输入特征x和输出y\n",
    "x_train = train_data.drop(['cnt'], axis = 1)\n",
    "x_test = test_data.drop(['cnt'], axis = 1)\n",
    "y_train = train_data['cnt'].values\n",
    "y_test = test_data['cnt'].values\n",
    "\n",
    "\n",
    "# 均值补偿\n",
    "y_train = y_train + (y_test.mean() - y_train.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 分别初始化对特征和目标值的标准化器\n",
    "ss_x = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "x_train = ss_x.fit_transform(x_train)\n",
    "x_test = ss_x.transform(x_test)\n",
    "\n",
    "#对y做标准化不是必须\n",
    "#对y标准化的好处是不同问题的w差异不太大，同时正则参数的范围也有限\n",
    "y_train = ss_y.fit_transform(y_train.reshape(-1, 1))\n",
    "y_test = ss_y.transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "      <th>spring</th>\n",
       "      <th>summer</th>\n",
       "      <th>autumn</th>\n",
       "      <th>...</th>\n",
       "      <th>sunny</th>\n",
       "      <th>fog</th>\n",
       "      <th>light_snow</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.027397</td>\n",
       "      <td>0.684932</td>\n",
       "      <td>0.486665</td>\n",
       "      <td>0.466835</td>\n",
       "      <td>0.643665</td>\n",
       "      <td>0.191403</td>\n",
       "      <td>3405.761644</td>\n",
       "      <td>0.246575</td>\n",
       "      <td>0.252055</td>\n",
       "      <td>0.257534</td>\n",
       "      <td>...</td>\n",
       "      <td>0.619178</td>\n",
       "      <td>0.339726</td>\n",
       "      <td>0.041096</td>\n",
       "      <td>0.142466</td>\n",
       "      <td>0.142466</td>\n",
       "      <td>0.142466</td>\n",
       "      <td>0.142466</td>\n",
       "      <td>0.142466</td>\n",
       "      <td>0.142466</td>\n",
       "      <td>0.145205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.163462</td>\n",
       "      <td>0.465181</td>\n",
       "      <td>0.189596</td>\n",
       "      <td>0.168836</td>\n",
       "      <td>0.148744</td>\n",
       "      <td>0.076890</td>\n",
       "      <td>1378.753666</td>\n",
       "      <td>0.431609</td>\n",
       "      <td>0.434789</td>\n",
       "      <td>0.437876</td>\n",
       "      <td>...</td>\n",
       "      <td>0.486255</td>\n",
       "      <td>0.474266</td>\n",
       "      <td>0.198785</td>\n",
       "      <td>0.350007</td>\n",
       "      <td>0.350007</td>\n",
       "      <td>0.350007</td>\n",
       "      <td>0.350007</td>\n",
       "      <td>0.350007</td>\n",
       "      <td>0.350007</td>\n",
       "      <td>0.352791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>431.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>0.321954</td>\n",
       "      <td>0.538333</td>\n",
       "      <td>0.135583</td>\n",
       "      <td>2132.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.479167</td>\n",
       "      <td>0.472846</td>\n",
       "      <td>0.647500</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>3740.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.656667</td>\n",
       "      <td>0.612379</td>\n",
       "      <td>0.742083</td>\n",
       "      <td>0.235075</td>\n",
       "      <td>4586.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.849167</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>6043.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          holiday  workingday        temp       atemp         hum   windspeed  \\\n",
       "count  365.000000  365.000000  365.000000  365.000000  365.000000  365.000000   \n",
       "mean     0.027397    0.684932    0.486665    0.466835    0.643665    0.191403   \n",
       "std      0.163462    0.465181    0.189596    0.168836    0.148744    0.076890   \n",
       "min      0.000000    0.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    0.000000    0.325000    0.321954    0.538333    0.135583   \n",
       "50%      0.000000    1.000000    0.479167    0.472846    0.647500    0.186900   \n",
       "75%      0.000000    1.000000    0.656667    0.612379    0.742083    0.235075   \n",
       "max      1.000000    1.000000    0.849167    0.840896    0.972500    0.507463   \n",
       "\n",
       "               cnt      spring      summer      autumn     ...      \\\n",
       "count   365.000000  365.000000  365.000000  365.000000     ...       \n",
       "mean   3405.761644    0.246575    0.252055    0.257534     ...       \n",
       "std    1378.753666    0.431609    0.434789    0.437876     ...       \n",
       "min     431.000000    0.000000    0.000000    0.000000     ...       \n",
       "25%    2132.000000    0.000000    0.000000    0.000000     ...       \n",
       "50%    3740.000000    0.000000    0.000000    0.000000     ...       \n",
       "75%    4586.000000    0.000000    1.000000    1.000000     ...       \n",
       "max    6043.000000    1.000000    1.000000    1.000000     ...       \n",
       "\n",
       "            sunny         fog  light_snow      Monday     Tuesday   Wednesday  \\\n",
       "count  365.000000  365.000000  365.000000  365.000000  365.000000  365.000000   \n",
       "mean     0.619178    0.339726    0.041096    0.142466    0.142466    0.142466   \n",
       "std      0.486255    0.474266    0.198785    0.350007    0.350007    0.350007   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      1.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "75%      1.000000    1.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "         Thursday      Friday    Saturday      Sunday  \n",
       "count  365.000000  365.000000  365.000000  365.000000  \n",
       "mean     0.142466    0.142466    0.142466    0.145205  \n",
       "std      0.350007    0.350007    0.350007    0.352791  \n",
       "min      0.000000    0.000000    0.000000    0.000000  \n",
       "25%      0.000000    0.000000    0.000000    0.000000  \n",
       "50%      0.000000    0.000000    0.000000    0.000000  \n",
       "75%      0.000000    0.000000    0.000000    0.000000  \n",
       "max      1.000000    1.000000    1.000000    1.000000  \n",
       "\n",
       "[8 rows x 33 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看各属性的统计特性\n",
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 测试数据单变量分布分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 目标y（租车人数）的直方图／分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(train_data[\"cnt\"].values, bins=50, kde=True)\n",
    "plt.xlabel('Median value', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单个特征散点图\n",
    "# plt.scatter(range(train_data.shape[0]), y_train,color='purple')\n",
    "plt.scatter(range(train_data.shape[0]), train_data[\"cnt\"].values,color='purple')\n",
    "plt.title(\"Distribution of cnt\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "各连续输入属性直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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HNlfsOugjxyUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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TVW2tqq0Mzh+8oqrmVqfcserz+/5PDE6Ik2QTg0Mwd021yvHr0+57gLMAkjyPQaDPT7XK1bEH+M1utMsZwENVdbD3q1f7rO8SZ3m/yuAs+Du7bX/C4C8xDL7YfwS+BnweePZq1zyldn8auB/Y1z32rHbN02r7YfveQCOjXHp+7wHeB3wJuB14zWrXPKV2nwp8lsEImH3Ay1e75jG1+6PAQeAHDHrjFwJvBN644Pv+QPfncvtyf9e9UlSSGrHWDrlIklbIQJekRhjoktQIA12SGmGgS1IjDHQ1Kcnd3YVY0oZhoEtSIwx0SWqEga6WbetuEvBQd1OUY5K8PslNC3fqbprxs93ypUn+JsmnkjzczXL4zCR/neTbSb7cTcMgrTkGulr2amAH8CzgF+k/Y+OrgT9iMBnUI8DngC9061cyuBRfWnMMdLXs/VV1X1U9APwzsK3n666qqluq6vvAVcD3q+rDVfUYcDlgD11rkoGulv33guXvAU/r+br7Fyz/7yLrfd9HmioDXRvN/wBPfXwlyTNXsRZprAx0bTS3Ar+QZFuSYxjc6kxqgoGuDaWqvspg3u1PA3cCNx35FdL64XzoktQIe+iS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktSI/wdxhWO8YrbcdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = train_data.columns\n",
    "# print(columns)\n",
    "# 连续特征\n",
    "colu = ['temp', 'atemp', 'hum', 'windspeed']\n",
    "size_feature = columns.shape[0] - 1\n",
    "for i in range(0, size_feature):\n",
    "    if columns[i] in colu:\n",
    "        fig = plt.figure()\n",
    "        sns.distplot(train_data[columns[i]].values, bins=30, kde=False)\n",
    "        plt.xlabel(columns[i], fontsize=12)\n",
    "        plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "各离散输入属性直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VBUSS44APAecBpwFvTHLaZKuSpOm0qgICOAPYW1XfqKr/A3wM2DrhmiRpKq22gFgP7Btanu/aJEljttpeGJRGWz2pQ7ID2NEtPpbk671XNT3WAt+ZdBGrQT6wfdIl6Mn8bS56T+s/k0/bj43SabUFxDywcWh5A7B/uENV7QR2jrOoaZFkrqpmJ12HtJS/zclYbaeY/hzYnOSlSZ4LXAjsnnBNkjSVVtURRFUdSvJW4L8BxwFXVdVdEy5LkqbSqgoIgKq6Ebhx0nVMKU/dabXytzkBqaqVe0mSps5quwYhSVolDIgptNJwJklOTPLxbv2tSTaNv0pNmyRXJTmY5M4jrE+Sy7vf5R1JXjXuGqeNATFlRhzO5GLgwar6ceAy4DfGW6Wm1NXAucusPw/Y3E07gCvGUNNUMyCmzyjDmWwFdnXznwDOSXJUns6RjqSqPg88sEyXrcA1NXALsCbJuvFUN50MiOkzynAm/79PVR0CHgZ+dCzVSUfmUDxjZkBMnxWHMxmxjzRu/i7HzICYPisOZzLcJ8nxwI+w/KG/NA6j/HZ1FBkQ02eU4Ux2A4uj1b0e+Ez5wIwmbzdwUXc305nAw1V1YNJFHctW3ZPU6teRhjNJ8j5grqp2A1cC1ybZy+DI4cLJVaxpkeSjwFnA2iTzwHuAEwCq6ncZjLBwPrAX+B7w5slUOj18klqS1OQpJklSkwEhSWoyICRJTQaEJKnJgJAkNRkQ0giSfC7JP5p0HdI4GRCaOknuTfL9JI8NTadMui5ptTEgNK1eV1XPH5qO2pAN3fAkR83R3p80KgNC6iQ5M8kXkzyU5MtJzlrS5WVJbkvycJIbkpzcbbcpSSW5OMl9wGeSnNU9DTy8/3uTvKabPyPJn3XfdSDJ73RDnyz2rSSXJLkHuCfJh5J8cMn+/jjJ2/v4ZyGBASEBkGQ98J+BXwdOBn4FuD7JzFC3i4BfAk4BDgGXL9nN3wNeAbx2hK88DPwLYC3wt4BzgH+6pM824GcYvNhpF4OXOz2nq3dtt81HR/sLpafPgNC0+lT3f+8PJfkU8Cbgxqq6sap+UFU3AXMMxv5ZdG1V3VlV3wX+NfCG7g19i95bVd+tqu+v9OVVdXtV3VJVh6rqXuD3GATMsH9XVQ9U1fer6jYG7+U4p1t3IfC5qrr/mfzx0igMCE2rbVW1ppu2AT8GXDAUGg8BfwcYfmPZ8Mtq/oLBQHJrj7B+WUlenuTTSb6d5BHg3y7ZV2t/uxgEGd3ntaN+n/RMGBDSwD4GRwhrhqYfrqr3D/UZfhfBqcD/Bb4z1DY88uV3gectLnRHGsOnq64AvgZsrqoXAu/mqS/EWTqS5h8AW5O8ksGprE+N/NdJz4ABIQ38AfC6JK9NclySk7oLzRuG+rwpyWlJnge8D/hEVR0+wv7+J3BSkp9NcgLwr4ATh9a/AHgEeCzJTwD/ZKUCq2qewfs8rgWuH+VUlvRXYUBIQFXtA7Yy+D/5BQZHFO/gyf+OXAtcDXwbOAn458vs72EGF51/H/gWgyOK4buafgX4h8CjwIeBj49Y6i7gp/D0ksbA90FIzyJJ/i6Do51NVfWDSdejY5tHENKzRHeq6m3A7xsOGgcDQnoWSPIK4CEGd1X9xwmXoynhKSZJUpNHEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElN/w8IfQ9RKkAUIwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yEPAx4FTgWOCsJMeOtypJmkyLKiCAE4BtVfWdqvpf4Epg7ZhrkqSJtNgCYjmwfWB7pmuTJI3YYvvJ0TTa6pc6JOuB9d3mw0m+1XtVk2Mp8MNxF7EY5APrxl2Cfpl/m3Pe1fpncp/99jCdFltAzAArB7ZXADsGO1TVBmDDKIuaFEk2V9X0uOuQ9uTf5ngstktMXwVWJzk6yZOBM4FNY65JkibSohpBVNWjSd4I/CtwEHBpVd015rIkaSItqoAAqKrrgevHXceE8tKdFiv/NscgVbVwL0nSxFls9yAkSYuEATGBFnqdSZJDknyq239rklWjr1KTJsmlSXYl2bqX/Unyke7v8s4kx4+6xkljQEyYIV9nci7wQFUdA1wEvH+0VWpCXQacMs/+U4HV3bIeuHgENU00A2LyDPM6k7XAxm79auDkJAfk6Rxpb6rqJuDH83RZC1xes24BDkuybDTVTSYDYvIM8zqT/+9TVY8CDwLPGkl10t75Kp4RMyAmz4KvMxmyjzRq/l2OmAExeRZ8nclgnyQHA89k/qG/NArD/O3qADIgJs8wrzPZBMy9re7VwBfLB2Y0fpuAc7rZTCcCD1bVznEX9US26J6kVr/29jqTJBcAm6tqE3AJcEWSbcyOHM4cX8WaFEk+CbwEWJpkBngXsASgqv6e2TcsnAZsA34OvG48lU4On6SWJDV5iUmS1GRASJKaDAhJUpMBIUlqMiAkSU0GhNSTJJclee+465D2lwEh7aMkX07y+nHXIfXNgJAkNRkQ0n5K8qdJbt6jrZIc0+i7NcnLB7aXJPlhkjWjqFXaHwaENBqXA2cPbJ8G7KyqLWOqR1qQASGNxj8CpyV5Rrf9WuCKMdYjLciAkEagqnYA/w68KslhzP585ifGW5U0P9/mKu2/nwFPndtI8pwF+m8EXs/s/3dfqarv91ib9GtzBCHtv68DL0yyJsmhwLsX6P8Z4HjgTczek5AWNQNC2j9VVf8JXAB8Afg2cPMCB/w3cA1wNPDp3iuUfk3+HoS0j5LcAVxQVZ/Zj2PfCTy/qs5esLM0Zt6DkPZBkhcCLwC+th/HHgGcy+wMJmnR8xKTNKQk7wc+D7ytqr63j8f+ObAd+FxV3dRHfdKB5iUmSVKTIwhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkpv8DdeKEkPrOv8kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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t4ANV9XdDzyK5ByHNiCTnMXq940tDzyKBXzkqzYQk/wCsAt5fVT8aeBwJ8BCTJGkvPMQkSWoyEJKkJgMhSWoyEJKkJgMhSWoyEJKkpv8DFnZIxvfmEzQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0, size_feature):\n",
    "    if columns[i] not in colu and columns[i] != 'cnt':\n",
    "        fig = plt.figure()\n",
    "        sns.countplot(train_data[columns[i]].values);\n",
    "        plt.xlabel(columns[i], fontsize=12);\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 两两特征相关性分布分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33, 33)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_corr = train_data.corr().abs()\n",
    "train_data_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3TpvWrRo6jGbL+Gp8ttiiW15/7a28Of/75ttuuzd79u5Zpc2ee/ZM/5tuT5LceefQBYX1z42fkKnl05MkEyZMyjLLLJOWLVtm7S5r5tVX3sjMme8nSUaOfCR9996tHnvVdFWu53hn4XqOO4Zk1z12qtKm1+47Z+DNi6xJXLCeY9tMfPHL13N8ocs31syqq62SJx51o+oloS6u/a6zbpc8+/Tz+eTjT1JRUZEnHn06vfbcqfqvpoa22nKzKutGB9x6d/r02bVKmz59euXGGwcmSW6/fXB23mn7+dt3zYBb785nn32WN998J6+99ma22nKzKsfuvPP2ef31t/L225OTVH6v2Xr+PKVNm1aZUj6trrvYpMhX4/bFOu0v5hwDB96b3tXmHL1790z//pVzjjvuGJLu3SvXaY8f/2LKv2TOkSRjxz6TqVOn12NPmgf5Wvo5JzYu8tV0LFZ3dOvdX1539EUu7xicnebnckHdkbWIAMB8NapkLRaLFUm+XWjCt3Dv0GH1lE+euuD11CnT0qFD1cKo9ou0qaioyAcf/Csrr1K1sLTP93bL3XcMWez9W7dulR677pgxo5+og+ibn44d2+edd6cseD15cnk6dWy/WJt357epzNeHadt25Rq9f5s2rbPnnj0ycuQjSy5okiSrt18t0yYvvDAzvXxGVm+/ao2Pb9dx9dw84voMfvr23HBF/8yc9l5dhMl87TqslmlTFuZrWvn0rN5htVq/z0abbZgWLVrknTcnL8nwmK99h3aZvMhnWPmUqWm/2GdYu0yp8hn2YVZZZaWs03WtFFPMzbdfk/tH35ajj134ZPVTfn1M/nrl9fn3xx/XT0eaiQ4d22Xyu+ULXk+ZPHWxm/V0XKRNRUVFPpjzr6zSduUsv/xyOe5XP8vFv/1zvcbcnPzpj+fm1NPOX2wRw5c5/LAfZth9lU977tljh3Tt2iXbbLtnvr1Fr2y+2Sb57vaL37ipU8f2efedReYw7y6cwyy77DJ5/LEheeThe7PXXrsudiy1s2qHVTO9fMaC1zOmzsyqHWo+5/jCTnt1z4hqT/VmyVu1w6qZUSVfM7Jqh7a1fp+d9uqekfJVL1Zt3zbTpyySs/KZaVvDef3EcRPz7GPjc+tTN+fWp2/OU6OfztuvvlNXoZKkRfu2+XyRMTa3fGZatFt8jLXebdt0HfLnrHHlaWmxyDmzRYdV03XIn7PeI/0y8+rbM3f6+/USd3PSrsNqKZ+y6HWp6Wm32Jx+tSrXpT6cf13qpRcnpcduO6a0tDSd1+yYjbptkA6d3AyyLrXvsHrKpyz88rt8yrS0q/a3crtq1xG/yNfQex7Iv//9SR5/cXjGPDs01175j8yZ/UHWWLtT3n9vVn7353Nz74M357eXnpXlll+2XvvVVBlfjdvXGW8TX5yUnrt1XyR/G6Zjp6rXkPn6jLHGy/ha+hlfjY+cNV7OiUs/4wvqjvHV+HTs2D7vTl74ffPkyVPTcbE1U+0WtKmoqMicL1kztffeu+e58S/ms88+y+uvvZl111sna67ZKaWlpenTp2c6de5Y951pBirXaizMV/mUaWnfoeo4ad9x9cXWc6y8ykr5xjprJ8Vi+t92TYaNGpijFlnP8YW+++6Ze+4YVqd9aE7q4trvpImvZattNs9KK7fJssstm+49tk+HjuaL/6uOnRauCU3+w7rRTgvXllZUVGTOnA/Stu3Kles1qh1bfe5+wP59M2DAXQten3Di2bnot2fk9deezMUXnZkzzvhtXXSryZKvxq1yDfaic47ydOpU+3Xa3/veHhk/f85B3ZGvpZ9zYuMiX01Hp44d8u471f6G7tShWpuF59DKv6E/qHHdEQDQvJTVou0zSe4uFAoDk3z0xcZisXjHlzUuFAo/TfLTJFlp+Q5ZYZlVvk6cde9L7jNQLBarNfnvbTb79sb55OOP8/LEV6u0KS0tzZV/+12uu6Z/3n7r3SUUcPP2VbmobLP4cdXbfJnS0tLc+I8rcuWV/fLGG2//zzHyH3xpXmp++LQp0/PDXQ7Lqu3a5o/9LsyIQaPyvqdo1p0ajLWvsurqbXPhn8/KGcf+ptbHUjNfek6sSZtiMaWlZdlq682z+0775+OPP8mtd1+X556dkFmzZmftb6yZs0+/OJ3X9CX7klSTz7D/NPZO/fVx+cuV/fLRR/+uq/CatT336JHp02dm3DPPZ8cdtvmvbf/v//bJFt/ulp122TdJ0rPHjunZY8c89eT9SZIVV1g+Xbt2ycNjqt6I6b/lv8s6W6W8fFq6dFkzw++7NS+88FJef/2tJdG1ZqnwJZOO2n4OrbL6KvnG+l3y5OinllRY1ML/kq8u668tX/XkS+/VV8OcdVy7Y9bqukZ+sNWBSZLf/fO3eeo7G+X5J15YkiHyVarl68MRYzPn3tEpfjY3K//f7un0+1/lzYN+nST5vHxmXt3jmJStvkrWvPqMzBn6SCpmzm6IqJusGo2p/zCPGNj/7qyzbpfc9cBNmfxuecaNHZ+Kioo6ipQkNbq28Z/mfd02/1bmVVRkm416pc1KrTJg0HV5ZPQTKSsry7c2WT/nnHpxxo97IWdecFKOPPZHueSiq+qoE82H8dXIfY3xNrD/3em6bpfc/UD/BfmbK39LnDHWiBlfSz3jq/GRs0bMOXGpZ3xB3TG+Gp+arZn672022OCbOe/8U9K3zyFJktmzP8gvjzszN9x4RebNm5cnHh+XLl3WWMKRN081Wb/2Zd9rplhMaVlpttx68+yx8wGV6znu+nuef/bFjHlo4XfQfffZPcceeeqSDrv5qoNrv6+98kauvvz6/OP2v+TfH32cl16clIqKuXXUgabvfz8HfvWxLVq0SO/evfLrRYrffvbTQ3LiSefkzjuHZL/9+uSaq/+Y3Xb/wdfpQrMiX41bjT7DajDnOP/8U9O790FLPD6qkq+ln3Ni4yJfTceSOD8CAHyhNsX4qyR5L8nOi2wrJvnSYvxisXhNkmuSpPMqGy31M5HyKdPSYZE7TrXv2C5Tp8740jblU6altLQ0rVuvmNmz5izYv9c+u+eu24cu9t4XX3pO3njt7fz9rzfVXQeamcmTy7PGIndg7tSpQ6aUT6vWZmo6d+6YyZOnzs9Xq7z//lcXC1x11cV59dU38ucr/r7E4yaZXj4j7TotvIv66h1Wy4xpM2v9PjOnvZfXXn4zm32nW0YMHrUEI2RR06ZMT7uOC/PVrsPqmTG15vlaYcXlc+VNf8yfL74mz417sS5CJEn5lKlV7mLaoWP7TCufvlibjlU+w1pl1qw5KZ8yNY898uSC8+ODwx/Kxt02zEcffZRNun0rY58bntLS0qy6WtvcPuj67Nv7sPrsWpM0ZfLUdOq88K6KHTu1z9Rq+fqizZQp8z/D2qyYWe/PzhZbdkvfvXfLub85OW3atM68efPy6aef5dqrb6zvbjRJ2267Rfr07pXdd9s5yy67TFq3bpUbrr88hx52bJV2u+z83Zx26rHZeZd9F9wFuFAo5OLfXZFr/1Z1vnfUkYfmiCMqi0377HVw3p1cns5rLDKH6bxwDlM+/9833ng7ox96LJtuupFi/K9hRvmMrL7IUwpWa79q3pv6Xq3eY6c+O2bMsEdSMdfCsbo2s3xmVquSr9Xy3tTaPXm7e58dMmbYo/JVT2aUz8zqHRfJWYdV8960mo2x7XfdNhOeeSmf/PuTJMnYkU9lg802UIxfhz6f+l5aLDLGyjqsms+rPd2+YvaHC/4/65b70v6UwxZ7n7nT38+nr7yVFbb8Vj4Y+kidxdscTZ0yvcqTcNp3XD3Tql2XmjplejrMnzuWlpam1SLXpS44448L2g0c0i9vvubmgnWpMl8Ln17VoWO7TF8sX9O+NF977bt7Ro94NHPnzs17M2fl6Seezcabbpixj43L1CnTM35c5blw2L0P5MjjDq/XfjVVxlfj9nXGW5KcXyV/18tfHTDGGi/ja+lnIj7hiwAAIABJREFUfDU+ctZ4OScu/YwvqDvGV+MzeXJ5Oi/yFL9Ondov+J5xYZup6dypQ6bMXzPVZpE1Ux07tc8/b7k6P/3xCVUeUjJ0yIgMHTIiSXL4j37oxgpLSPmUaVWeutihY7tMm1p9Pce0/7CeY1oef+SpzFqwnuPhbNRtwwXF+BtutF7Kykrz/PgJ9dehJq4urv2+89bk3Nr/rtzav/LJpyf++heZOqXqmKXmJr9bns5ftW703cq1pZMnl1eeA9u0zvvvz6pcr1Ht2PJFcrHbbjvlmWeez/TpC9fGHXzw9/Or489Kktx22725+q+/r6uuNUny1bhVrsFedM7RIVOmVJ9zlP/HddqdOrXPgAHX5Mc/Pt6D0eqBfC39nBMbF/lqOirX7Fb7G3rK1MXbdO6wMJetW9eo7ggAaH5KatqwWCwe/iU/P6rL4OrT+HEvpMs31swaa3ZKixZl6bvP7hk+bGSVNsOHjsz3f9A3SbJn31555OGFd5ktFArp3bdX7rmjajH+Sacfk9atV8zZp19U951oRp56any6dl07a6+9Rlq0aJH9v79XBg0aXqXNoEHDc/BB+yVJ9tlnz4wa9dWFAuecc1LatG6VE048py7CJsmEZ1/KGl06p+MaHVLWoiy9+u6Sh+4bU6NjV++wWpZZtmWSpFWbFdNty419kVvHXnx2Ytb6xhrptGZlvnbbu0dG3f9wjY4ta1GWS/tdnHsHDs3wex+s40ibt2fHvZAu66yVNdbqlBYtWqTvvrvnvqFVP8PuGzoy+/9w7yRJ7769Fnw5O2rEI9nwW+tlueWWTWlpabbebstMevnV/OO6Adlsg+7ZapOe6bv7QXn91TcV4i8h455+Luuss1bWXKtzWrRokX3223PBooYvDBsyIj888HtJkr7f2y0PjX48SbJHrx+m27e6p9u3uucvV12fP/3hLwrxl6Bfn3FR1v7GFum67tY58KCjM3LkI4sV4m+66bdy1ZUX5Xv7HJ4ZMxYWnd4/fFQOP+yArLDC8km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\n",
      "text/plain": [
       "<Figure size 5760x5760 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(80, 80))\n",
    "sns.heatmap(train_data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(train_data_corr, mask=train_data_corr < 1, cbar=False)\n",
    "\n",
    "plt.savefig('bike_coor.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temp and atemp = 1.00\n",
      "sunny and fog = 0.91\n",
      "atemp and cnt = 0.78\n",
      "temp and cnt = 0.77\n",
      "cnt and spring = 0.72\n",
      "temp and autumn = 0.67\n",
      "atemp and autumn = 0.65\n",
      "atemp and spring = 0.65\n",
      "temp and spring = 0.64\n",
      "workingday and Sunday = 0.61\n",
      "workingday and Monday = 0.60\n",
      "hum and sunny = 0.56\n",
      "winter and October = 0.54\n",
      "spring and January = 0.53\n",
      "winter and November = 0.53\n",
      "summer and May = 0.52\n",
      "autumn and August = 0.52\n",
      "autumn and July = 0.52\n",
      "summer and April = 0.52\n",
      "spring and February = 0.50\n"
     ]
    }
   ],
   "source": [
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "size = train_data_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (train_data_corr.iloc[i,j] >= threshold and train_data_corr.iloc[i,j] < 1) or (train_data_corr.iloc[i,j] < 0 and train_data_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([train_data_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (columns[i],columns[j],v))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "小结：    \n",
    "&emsp;由得到的两两特征相关性系数计算结果可以看出，下列两两特征间强相关（相关性系数大于0.5）    \n",
    "<font face=\"微软雅黑\">\n",
    "&emsp;&emsp;气温temp和体感温度atemp、秋天autumn、春天spring强相关；     \n",
    "&emsp;&emsp;晴天sunny和雾天fog强相关；    \n",
    "&emsp;&emsp;是否工作日workingday和周天Sunday、周一Monday强相关；    \n",
    "&emsp;&emsp;湿度hum和晴天sunny强相关；    \n",
    "&emsp;&emsp;冬天winter和十月October、十一月November强相关；    \n",
    "&emsp;&emsp;春天spring和一月January、二月February强相关；    \n",
    "&emsp;&emsp;夏天summer和五月may、四月April强相关；   \n",
    "&emsp;&emsp;秋天autumn和八月August、七月july强相关；   \n",
    "</font>\n",
    "&emsp;因此，   \n",
    "&emsp;&emsp;用气温temp代表体感温度atemp、秋天autumn、春天spring特征，即从数据集中剔除秋天autumn、春天spring特征；   \n",
    "&emsp;&emsp;用是否工作日workingday代表周天Sunday、周一Monday特征，即从数据集中剔除周天Sunday、周一Monday特征；   \n",
    "&emsp;&emsp;用湿度hum代表晴天sunny、雾天fog，即剔除晴天sunny、雾天fog特征；   \n",
    "&emsp;&emsp;用冬天winter代表十月October、十一月November特征，即从数据集中剔除十月October、十一月November特征；   \n",
    "&emsp;&emsp;用春天spring代表一月January、二月February特征，即从数据集中剔除一月January、二月February特征；   \n",
    "&emsp;&emsp;用夏天summer代表五月may、四月April特征，即从数据集中剔除五月may、四月April特征；  \n",
    "&emsp;&emsp;用秋天autumn代表八月August、七月july特征，即从数据集中剔除八月August、七月july特征；  \n",
    "<font color=#00008B face=\"黑体\">\n",
    "&emsp;综上所述，剔除的特征有，体感温度atemp、秋天autumn、春天spring、晴天sunny、雾天fog、周天Sunday、周一Monday、一月January、二月February、四月April、五月May、七月July、八月August、十月October、十一月November。\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3694: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['holiday', 'workingday', 'temp', 'hum', 'windspeed', 'summer', 'winter',\n",
      "       'March', 'June', 'September', 'December', 'light_snow', 'Tuesday',\n",
      "       'Wednesday', 'Thursday', 'Friday', 'Saturday'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# 删除相关性强的部分特征\n",
    "del_feature = ['atemp','autumn', 'spring', 'Sunday', 'Monday', 'sunny', 'fog', 'January', 'February', 'April', 'May', 'July', 'August', 'October', 'November']\n",
    "# print(train_data.head())\n",
    "train_data_temp = train_data\n",
    "train_data_temp.drop(['cnt'], axis = 1, inplace=True)\n",
    "columns = np.delete(columns, 6) \n",
    "# print(columns)\n",
    "index = []\n",
    "for i in range(0, size_feature):\n",
    "    if columns[i] in del_feature:\n",
    "        train_data.drop([columns[i]], axis = 1, inplace=True)\n",
    "        index.append(i)\n",
    "x_train = np.delete(x_train, index, axis = 1)      \n",
    "x_test = np.delete(x_test, index, axis = 1)  \n",
    "columns = np.delete(columns, index) \n",
    "print(columns)\n",
    "# print(index)        \n",
    "# print(train_data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3、确定模型类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.657824\n",
      "The r2 score of RidgeCV on train is 0.791163\n"
     ]
    }
   ],
   "source": [
    "#岭回归／L2正则\n",
    "#class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, \n",
    "#                                  normalize=False, scoring=None, cv=None, gcv_mode=None, \n",
    "#                                  store_cv_values=False)\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "#设置超参数（正则参数）范围\n",
    "# alphas = [ 0.001, 0.01, 0.1, 1, 10,100, 1000]\n",
    "alphas = [ 0.01, 0.1, 1, 10, 100]\n",
    "# n_alphas = 20\n",
    "# alphas = np.logspace(-5,2,n_alphas)\n",
    "\n",
    "#生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#模型训练\n",
    "ridge.fit(x_train, y_train)    \n",
    "\n",
    "#预测\n",
    "y_test_pred_ridge = ridge.predict(x_test)\n",
    "y_train_pred_ridge = ridge.predict(x_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print(\"The r2 score of RidgeCV on test is %f\" % r2_score(y_test, y_test_pred_ridge))  \n",
    "print(\"The r2 score of RidgeCV on train is %f\" % r2_score(y_train, y_train_pred_ridge)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳alpha 为: 1.0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef_ridge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>temp</td>\n",
       "      <td>[0.7678945424263537]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>winter</td>\n",
       "      <td>[0.33679449973165343]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>summer</td>\n",
       "      <td>[0.18867148513885024]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>September</td>\n",
       "      <td>[0.10593597393964116]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>June</td>\n",
       "      <td>[0.01631461562850678]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>December</td>\n",
       "      <td>[0.008970239678639302]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>[0.00849334646612565]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>workingday</td>\n",
       "      <td>[0.0030069990580789963]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>[0.00035952273128359025]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>[-0.005566488246845136]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>[-0.010368024901160844]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Friday</td>\n",
       "      <td>[-0.01338485525756239]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>March</td>\n",
       "      <td>[-0.04994661735960815]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>holiday</td>\n",
       "      <td>[-0.05238043850914487]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>[-0.14732940256679772]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>light_snow</td>\n",
       "      <td>[-0.18961586010453058]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hum</td>\n",
       "      <td>[-0.1982817157556762]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       columns                coef_ridge\n",
       "2         temp      [0.7678945424263537]\n",
       "6       winter     [0.33679449973165343]\n",
       "5       summer     [0.18867148513885024]\n",
       "9    September     [0.10593597393964116]\n",
       "8         June     [0.01631461562850678]\n",
       "10    December    [0.008970239678639302]\n",
       "16    Saturday     [0.00849334646612565]\n",
       "1   workingday   [0.0030069990580789963]\n",
       "13   Wednesday  [0.00035952273128359025]\n",
       "12     Tuesday   [-0.005566488246845136]\n",
       "14    Thursday   [-0.010368024901160844]\n",
       "15      Friday    [-0.01338485525756239]\n",
       "7        March    [-0.04994661735960815]\n",
       "0      holiday    [-0.05238043850914487]\n",
       "4    windspeed    [-0.14732940256679772]\n",
       "11  light_snow    [-0.18961586010453058]\n",
       "3          hum     [-0.1982817157556762]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('最佳alpha 为:', ridge.alpha_)\n",
    "\n",
    "# 看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "# print(columns)\n",
    "# print(ridge.coef_.T)\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_ridge'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 LASSO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.658325\n",
      "The r2 score of LassoCV on train is 0.791163\n"
     ]
    }
   ],
   "source": [
    "#### Lasso／L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "#设置超参数搜索范围\n",
    "# alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "#生成一个LassoCV实例\n",
    "# lasso = LassoCV(alphas=alphas)  \n",
    "# lasso = LassoCV()  \n",
    "lasso = LassoCV(eps=0.001, n_alphas=500, max_iter=5000) \n",
    "\n",
    "#训练（内含CV）\n",
    "lasso.fit(x_train, y_train)  \n",
    "\n",
    "#测试\n",
    "y_test_pred_lasso = lasso.predict(x_test)\n",
    "y_train_pred_lasso = lasso.predict(x_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print(\"The r2 score of LassoCV on test is %f\" % r2_score(y_test, y_test_pred_lasso))  \n",
    "print(\"The r2 score of LassoCV on train is %f\" % r2_score(y_train, y_train_pred_lasso)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳alpha 为:  0.0007712141978934428\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>coef_lasso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>temp</td>\n",
       "      <td>[0.7678945424263537]</td>\n",
       "      <td>0.770142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>winter</td>\n",
       "      <td>[0.33679449973165343]</td>\n",
       "      <td>0.337347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>summer</td>\n",
       "      <td>[0.18867148513885024]</td>\n",
       "      <td>0.188236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>September</td>\n",
       "      <td>[0.10593597393964116]</td>\n",
       "      <td>0.104826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>June</td>\n",
       "      <td>[0.01631461562850678]</td>\n",
       "      <td>0.015055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>[0.00849334646612565]</td>\n",
       "      <td>0.010398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>December</td>\n",
       "      <td>[0.008970239678639302]</td>\n",
       "      <td>0.008472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>[0.00035952273128359025]</td>\n",
       "      <td>0.002084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>workingday</td>\n",
       "      <td>[0.0030069990580789963]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>[-0.005566488246845136]</td>\n",
       "      <td>-0.002640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>[-0.010368024901160844]</td>\n",
       "      <td>-0.007329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Friday</td>\n",
       "      <td>[-0.01338485525756239]</td>\n",
       "      <td>-0.010262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>March</td>\n",
       "      <td>[-0.04994661735960815]</td>\n",
       "      <td>-0.048887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>holiday</td>\n",
       "      <td>[-0.05238043850914487]</td>\n",
       "      <td>-0.052893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>[-0.14732940256679772]</td>\n",
       "      <td>-0.147064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>light_snow</td>\n",
       "      <td>[-0.18961586010453058]</td>\n",
       "      <td>-0.189551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hum</td>\n",
       "      <td>[-0.1982817157556762]</td>\n",
       "      <td>-0.198073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       columns                coef_ridge  coef_lasso\n",
       "2         temp      [0.7678945424263537]    0.770142\n",
       "6       winter     [0.33679449973165343]    0.337347\n",
       "5       summer     [0.18867148513885024]    0.188236\n",
       "9    September     [0.10593597393964116]    0.104826\n",
       "8         June     [0.01631461562850678]    0.015055\n",
       "16    Saturday     [0.00849334646612565]    0.010398\n",
       "10    December    [0.008970239678639302]    0.008472\n",
       "13   Wednesday  [0.00035952273128359025]    0.002084\n",
       "1   workingday   [0.0030069990580789963]   -0.000000\n",
       "12     Tuesday   [-0.005566488246845136]   -0.002640\n",
       "14    Thursday   [-0.010368024901160844]   -0.007329\n",
       "15      Friday    [-0.01338485525756239]   -0.010262\n",
       "7        March    [-0.04994661735960815]   -0.048887\n",
       "0      holiday    [-0.05238043850914487]   -0.052893\n",
       "4    windspeed    [-0.14732940256679772]   -0.147064\n",
       "11  light_snow    [-0.18961586010453058]   -0.189551\n",
       "3          hum     [-0.1982817157556762]   -0.198073"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print (\"最佳alpha 为: \", lasso.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lasso'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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